In furnaces in the iron and steel-making and glass industries, the flow rates of fuel into the heated zones are established and adjusted in the heating regulation loops. The block diagram of the control of the heating of a zone is represented in FIG. 1 of the appended drawings. This figure relates to a an entirely conventional regulating system and therefore will not be commented upon.
The performance of the regulating device, or controller depends on the proper choice and proper adjustment of its internal parameters. The most conventional solution consists of obtaining a set of fixed parameters of the regulator. To do this, a mathematical model of the process (a transfer function) must be available and formulae which relate the parameters of the regulator must then be applied. It should be borne in mind that these formulae always provide a compromise between the quality of the transient response and the steady state. Moreover, a conventional regulator whose parameters stem from these formulae is adapted only for processes which can be described by simple equations.
In reality, such is not the case for the furnace heating process which is very complex and cannot be modelled by a simple function.
Furthermore, such a process undergoes, in the course of time, various modifications or disturbances among which may be mentioned in particular:
shutdowns and restarts of production which generate transient steps, PA1 variations in production, i.e. products of different types, dimensions and masses enter the furnace in sequence. This involves continual changes of heat demand; PA1 variations in the rates of passage of products through the furnace. PA1 the inputs of the controller cannot be chosen, hence it is not possible to take into account parameters which appear to be useful to a specified application and which influence the process. The inputs of the fuzzy controller are standard variables such as error and differential of error; PA1 the rules cannot be adapted in accordance with knowledge of the process and its behavior, extracted from observations and experiments, since the rule base is also standard and fixed. PA1 detection of the state of the process to be controlled and regulated: transient state or steady state is selected, depending on the instructions/measurement discrepancy; PA1 selection of a control algorithm on the basis of the state (transient state or steady state), while ensuring a smooth changeover between the states, with no discontinuity of control; PA1 calculation of the parameters of the regulator on the basis of the specific requirements, taking into account the following factors: PA1 the error indicating the current divergence between the instruction and the measured temperature of the wall of the furnace; PA1 the variation in the temperature of the said wall measured in the given time step.
As a consequence a regulator with fixed parameters, even if "ideally" well adjusted, will never manage to exceed a certain limit of complexity and uncertainty of the process.
For these reasons, it has been expedient to investigate a regulation whose parameters may be adapted according to the given situation of operation rather than being constant.
Within the framework of conventional systems for automatic regulation, the idea of an auto-adaptive regulator has thus made its appearance. The guiding idea of this type of regulator is to adjust the coefficients of the controller on-line with the aim of adapting the regulating action to the conditions of operation. However, this idea is still based on the same principles as in the case of a non-adaptive regulator, i.e. on a necessarily simplified and still fixed mathematical model of the process, only the values of the parameters of which are adjusted on-line. instantaneous adjustment of the regulator still amounts to applying conventional, simple adaptation formulae which are based on a compromise response.
In this approach, the adjustment of the parameters stemming from conventional adaptation formulae is therefore still not optimized. Furthermore, since the model, even if its parameters are permitted to alter, has to be simple and cannot incorporate instantaneous structural changes caused by modifications and disturbances in the running of the process to be controlled and regulated, the model may momentarily not describe the system correctly, thus bringing about a significant degradation in the regulation.
The successful appearance has moreover been seen of novel control techniques, such as fuzzy logic, adapted to processes which are difficult to model.
A controller implementing the technique of fuzzy logic or fuzzy controller is based on a logic model which represents the strategy which would be implemented by an operator if the latter had to control the system manually. Intuitive control strategies can be approximated by fuzzy algorithms which provide a method for processing qualitative information in a rigorous manner.
Several fuzzy controllers are already available on the market. Although they apply fuzzy reasoning instead of being based on a mathematical model, they have a major drawback: being standard controllers they are therefore not optimal in respect of a specific system. They possess a standard and fixed rule base which is therefore not constructed in accordance with observations of the behavior of the given process. Indeed, regulation is based on control of the instruction/temperature measurement discrepancy, increasing the action in proportion to the discrepancy. Such a mode of regulation is not at all adapted to systems with significant inertia, as is the case in particular with a furnace.
By using standard fuzzy regulators two important advantages of the technique of fuzzy regulation are lost:
The same problem remains: a standard solution is available but, being standard, still provides some compromise and consequently this solution is not optimal and is not always suitable for a given situation.